The Federated Tumor Segmentation (FeTS) platform: An intuitive tool facilitating secure multi-institutional collaboration

联合肿瘤分割 (FeTS) 平台：促进安全多机构协作的直观工具

• 批准号: 10248412
• 负责人: Spyridon Bakas
• 金额: $ 35.8万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10248412
• 关键词: Adoption; Agreement; Algorithms; Anatomy; Appearance; Back; Benchmarking; Brain; Brain Neoplasms; Clinical; Collaborations; Communities; Consensus; Data; Data Set; Databases; Development; Diagnostic radiologic examination; Docking; Face; Feedback; Glioma; Goals; Gold; Hospitals; Image; Image Analysis; Individual; Institution; International; Intuition; Knowledge; Label; Learning; Legal; Lesion; Letters; Liver neoplasms; Location; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Neoplasms; Manuals; Medical Imaging; Modeling; Monitor; Multiple Myeloma; Observer Variation; Outcome; Ownership; Participant; Patients; Pattern; Performance; Phenotype; Privacy; Radio; Research; Research Personnel; Scanning; Secure; Shapes; Software Tools; Solid Neoplasm; Source Code; Structure; Technology; Testing; Training; Training Activity; Update; Validation; Variant; Work; X-Ray Computed Tomography; algorithm training; anticancer research; base; bone; clinical translation; clinically translatable; contrast enhanced; graphical user interface; human error; improved; interactive tool; light weight; open source; open source tool; predict clinical outcome; repository; segmentation algorithm; tool; tumor; user-friendly

ABSTRACT: Accurate segmentation of solid tumors is challenging, due to their heterogeneous shape, extent, and location, as well as their appearance variation caused by the diversity of medical imaging. Manual annotation is tedious, prone to misinterpretation, human error, and observer bias. All these factors hinder further image analysis towards understanding tumor radio-phenotypes, predicting clinical outcomes, and monitoring progression patterns. Computational competitions have been seeking optimal advanced computational segmentation algorithms (ACSAs) for specific abnormalities, by pooling multi-institutional data together and benchmarking ACSAs from international groups. Along these lines, we have been successfully leading the organization of the International Brain Tumor Segmentation (BraTS) challenge, since 2012, towards a publicly-available pooled dataset of 542 multi-parametric MRI scans of glioma patients from 19 institutions. In the summarized analysis of all BraTS results, we have shown that although individual ACSAs do not outperform the gold standard agreement across expert clinicians, their fusion does outperform it, in terms of both accuracy and consistency across subjects. Towards the wider application of these ACSAs, in 2017 we created the BraTS algorithmic repository to make available Docker containers of individual ACSAs, created by BraTS participants. However, fusion of these ACSAs is still out of reach for clinical researchers, as there is no graphical user interface (GUI) to facilitate it, and execution of such algorithms requires substantial computational background by the user. Furthermore, although competitions such as BraTS have shown promise, they cannot easily scale due to the requirement of pooling patient data from multiple institutions to a single location, that often faces legal, privacy, and data-ownership concerns. These concerns motivate distributed learning solutions, where the data are always retained within their institutions. We have been investigating such solutions to avoid the current paradigm of multi-institutional collaboration, i.e., data-sharing, and considering their potential multi-institutional adoption, with respect to privacy, scalability, and performance, we found federated learning (FL) to be most appropriate. In FL, each institution trains a model and shares it (without patient data) with an aggregation server, which then integrates institutional models in parallel and distributes back a consensus model. In this proposal, we focus on developing the open- source Federated Tumor Segmentation (FeTS) platform, which with a user-friendly GUI will aim at i) bringing pre- trained models of various ACSAs and their fusion closer to clinical experts, and ii) allowing secure multi- institutional collaborations via FL to improve these pre-trained models without sharing patient data, thereby overcoming legal, privacy, and data-ownership challenges. Successful completion of this project will lead to an easy-to-use potentially-translatable tool enabling easy, fast, objective, repeatable and accurate tumor segmentation, without requiring a computational background by the user, and while facilitating further analysis of tumor radio-phenotypes towards accelerating discovery.

摘要： 由于实体瘤的形状、范围和位置不均匀，因此其准确分割是具有挑战性的， 以及由医学成像的多样性引起的它们的外观变化。手动注释是繁琐的， 容易产生误解、人为错误和观察者偏见。所有这些因素都阻碍了进一步的图像分析 了解肿瘤放射表型，预测临床结果和监测进展 模式.计算竞赛一直在寻求最佳的高级计算分割 通过将多机构数据汇集在一起并进行基准测试， 来自国际团体的ACSA。沿着这些路线，我们成功地领导了 国际脑肿瘤分割（BraTS）挑战，自2012年以来，朝着公开可用的汇集 来自19家机构的542例胶质瘤患者的多参数MRI扫描数据集。在总结分析中， 所有的BraTS结果，我们已经表明，虽然个别ACSA不优于黄金标准协议 在专家临床医生中，他们的融合在准确性和一致性方面确实优于它， 科目为了更广泛地应用这些ACSA，我们在2017年创建了BraTS算法库， 提供由BraTS参与者创建的各个ACSA的Docker容器。然而，这些融合 ACSA对于临床研究人员来说仍然遥不可及，因为没有图形用户界面（GUI）来促进它， 这种算法的执行需要用户的大量计算背景。此外，虽然 像BraTS这样的比赛已经显示出了希望，但由于需要汇集，它们不能轻易扩展 将患者数据从多个机构传输到一个位置，通常面临法律的、隐私和数据所有权问题 性问题这些问题激发了分布式学习解决方案，其中数据始终保留在其 机构职能体系我们一直在研究这种解决方案，以避免目前的多机构模式， 协作，即，数据共享，并考虑到其潜在的多机构采用，在隐私方面， 可扩展性和性能，我们发现联邦学习（FL）是最合适的。在佛罗里达州，每个机构 训练一个模型，并与一个聚合服务器共享（没有患者数据），然后将机构 并行建模并分发回共识模型。在这份提案中，我们着重于发展开放的- 源联合肿瘤分割（FeTS）平台，其具有用户友好的GUI，旨在i）将预 各种ACSA的训练模型及其融合更接近临床专家，以及ii）允许安全的多- 通过FL进行机构合作，在不共享患者数据的情况下改进这些预先训练的模型， 克服法律的、隐私和数据所有权方面的挑战。该项目的成功完成将导致 一种易于使用的潜在可平移工具，可实现简单、快速、客观、可重复和准确的肿瘤 分割，而不需要用户的计算背景，同时便于进一步分析 肿瘤放射表型加速发现。